# Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach

**Authors:** Linyu Liu, Yu Pan, Xiaocheng Li, Guanting Chen  
**arXiv:** [2404.15993](https://arxiv.org/abs/2404.15993)  
**Date:** 2024-04-24  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.15993v1/

## Key points

- Authors explore challenges posed by large language models (LLMs) in generating reliable and accurate outputs
- Proposed supervised approach leverages labeled datasets to estimate uncertainty and improve calibration for LLMs
- Highlighted distinction between uncertainty estimation for LLMs and standard machine learning models, emphasizing hidden activations of LLMs
- Approach demonstrates improved uncertainty estimation across various tasks and is adaptable to different levels of model transparency
- Adaptability allows for strong performance based on accessibility of internal mechanisms within LLMs
- Practical solution offers promise for improving reliability and accuracy of large language models in various applications

## Summary

In their paper titled "Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach," authors Linyu Liu, Yu Pan, Xiaocheng Li, and Guanting Chen explore the challenges posed by large language models (LLMs) in generating reliable and accurate outputs. They propose a supervised approach that leverages labeled datasets to estimate uncertainty and improve calibration for LLMs. The authors highlight the distinction between uncertainty estimation for LLMs and standard machine learning models, emphasizing the valuable information contained in hidden activations of LLMs. Their approach effectively demonstrates improved uncertainty estimation across various tasks and is adaptable to different levels of model transparency. This adaptability allows for strong performance based on the accessibility of internal mechanisms within LLMs. Overall, this practical solution offers promise for improving the reliability and accuracy of large language models in various applications.